Dynamic analysis of the capital structure in technological firms based on their life cycle stages

Abstract

This article analyses the effect of a firm’s life cycle stages on the capital structure in tech versus non-tech firms using a wide sample of public companies from Europe. An innovative approach based on operating, investing and financing cash flows allows us to analyse differences in leverage and specify the differential role of significant drivers of the capital structure across stages in both sectors. Our results point to the information asymmetry factor posed by the pecking order as the predominant driver behind the differences in the effect of intangible assets and growth opportunities for tech firms in some stages, mainly maturity. Frank and Goyal’s (2003) test of the pecking order theory confirms the lower use of debt by tech firms during all life cycle stages. In addition, we find that the results obtained for tech firms are largely attributable to the behaviour of high-tech firms with the highest growth opportunities.

Este trabajo analiza el efecto del ciclo de vida sobre la estructura de capital en empresas tecnológicas y no tecnológicas usando una amplia muestra de empresas cotizadas europeas. Una clasificación innovadora basada en flujos operativos, de inversión y financieros nos permite analizar las diferencias de endeudamiento y especificar el papel diferenciador de los inductores significativos de la estructura de capital en las etapas del ciclo de vida en ambos tipos de empresas. Nuestros resultados señalan que el factor diferenciador predominante para las empresas tecnológicas es la información asimétrica (teoría de la jerarquía), que afecta a los activos intangibles y a las oportunidades de crecimiento, especialmente durante la madurez. Aplicando el test de Frank y Goyal (2003) para la teoría de la jerarquía, se confirma el menor uso de la deuda para las empresas tecnológicas en todas las fases del ciclo de vida. Además, encontramos que los resultados obtenidos para empresas tecnológicas se pueden atribuir en gran medida a las empresas de alta tecnología con mayores oportunidades de crecimiento.

Keywords:

• life cycle
• capital structure
• technology firms
• growth opportunities
• leverage
• pecking order theory

Palabras clave:

• Ciclo de vida
• Estructura de capital
• Empresas tecnológicas
• Oportunidades de crecimiento
• Apalancamiento
• Teoría de la jerarquía

JEL classification:

• D91
• D92
• G32
• L6

JEL clasificación:

• D91
• D92
• G32
• L6

Acknowledgements

The authors wish to thank Victor González (editor), two anonymous referees, Alberto de Miguel, María José Palacín and Manuel Ángel Fernández for their helpful comments. We are also grateful for the helpful suggestions provided on earlier versions of this article by the participants at the 2013 ACEDE Congress, the 2013 AECA Congress and the XXI Finance Forum.

Notes

1. Information and Communication Technology.

2. We distinguish tech firms’ life cycles (the time span between a firm’s introduction and its decline) and the tech sector’s life cycles (the time span between a technology generation’s emergence and its decline).

3. Based on the SIC, we have high-technology firms, including 272, 283, 355, 357, 360–367, 369, 381, 382, 386, 481, 484, 489, 573, 596, 621, 679, 733, 737, 738 and 873, and low-technology firms, including 020, 160, 170, 202, 220, 240, 245, 260, 300, 308, 324, 331, 356, 371, 399, 401, 421, 440, 451 and 541.

4. There are two reasons for focusing only on quoted firms: (1) the Dickinson model is generally applicable only to firms issuing the cash flow statement, and this is not mandatory for a significant portion of non-quoted firms; and (2) the definition of the life-cycle stages may vary considerably for quoted versus non-quoted firms, specially concerning introduction and growth.

5. González and González (2008) obtained a similar range (10–20%) for the long-term debt ratio in the European countries of our sample, even though they divide by the market value instead of the book value of assets.

6. Our coefficient without distinguishing stages is 0.58 for non-tech firms and 0.60 for tech firms, whereas the range of coefficients obtained in the comparable works are: 0.16–0.62 by González and González, for the countries included in our sample; 0.69–0.91 by Öztekin and Flannery, for the countries included in our sample; 0.69 by Rubio and Sogorb (2011); 0.31–0.72 by Rubio and Sogorb (2012); 0.62–0.65 by Flannery and Rangan (2006) and 0.75–0.78 by Lemmon et al. (2008). González and González (2008) and Öztekin and Flannery (2012) analysed similar periods (1995–2004 and 1991–2006, respectively). Both samples are international, including the European firms taken in our sample (except The Netherlands in the second work). Rubio and Sogorb (2011, 2012) analysed Spanish firms during a similar period (1995–2003 and 1995–2007, respectively). Flannery and Rangan (2006) and Lemmon et al. (2008) studied US firms in a remarkable longer period (1965–2001 and 1965–2003, respectively), very different from ours. In addition, the leverage variable used by these works is a market debt ratio, except for Öztekin and Flannery and Lemmon et al. that use both market and accounting debt ratios.

7. Maturity is the stage in which debt is used in a higher proportion, as evidenced in Table 7, for both tech and non-tech firms. However, we can appreciate in Table 6 that tech firms’ inductors of debt during maturity are only tangible assets and size.

Additional information

Funding

This work was supported by the Spanish Ministry of Science and Innovation [ECO2011-29144-C03-01] and the University of Leon [ULE2012-1]. Paula Castro also acknowledges the Grant FPU [AP2012-1959] from the Spanish Ministry of Education, Culture and Sports.